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Peak Detection Method Evaluation for Ion Mobility Spectrometry by using Machine Learning Approaches

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44594

Hauschild,  Anne-Christin
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44085

Baumbach,  Jan
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Zitation

Hauschild, A.-C., Kopczynski, D., D'Addario, M., Baumbach, J. I., Rahmann, S., & Baumbach, J. (2013). Peak Detection Method Evaluation for Ion Mobility Spectrometry by using Machine Learning Approaches. Metabolites, 3(2), 277-293. doi:10.3390/metabo3020277.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0015-84FC-4
Zusammenfassung
Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors� results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.